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Harrie Oosterhuis
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Multileave gradient descent for fast online learning to rank
A Schuth, H Oosterhuis, S Whiteson, M de Rijke
Proceedings of the Ninth ACM International Conference on Web Search and Data …, 2016
792016
Differentiable unbiased online learning to rank
H Oosterhuis, M de Rijke
Proceedings of the 27th ACM International Conference on Information and …, 2018
562018
To Model or to Intervene: A Comparison of Counterfactual and Online Learning to Rank from User Interactions
R Jagerman, H Oosterhuis, M de Rijke
Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019
512019
Keeping dataset biases out of the simulation: A debiased simulator for reinforcement learning based recommender systems
J Huang, H Oosterhuis, M De Rijke, H Van Hoof
Fourteenth ACM conference on recommender systems, 190-199, 2020
422020
Probabilistic multileave for online retrieval evaluation
A Schuth, RJ Bruintjes, F Buüttner, J van Doorn, C Groenland, ...
Proceedings of the 38th international ACM SIGIR Conference on Research and …, 2015
322015
FOCUS: Flexible optimizable counterfactual explanations for tree ensembles
A Lucic, H Oosterhuis, H Haned, M de Rijke
Proceedings of the AAAI Conference on Artificial Intelligence 36 (5), 5313-5322, 2022
272022
Policy-aware unbiased learning to rank for top-k rankings
H Oosterhuis, M de Rijke
Proceedings of the 43rd International ACM SIGIR Conference on Research and …, 2020
272020
Probabilistic multileave gradient descent
H Oosterhuis, A Schuth, M Rijke
European Conference on Information Retrieval, 661-668, 2016
272016
The Potential of Learned Index Structures for Index Compression
H Oosterhuis, JS Culpepper, M de Rijke
Australasian Document Computing Symposium (ADCS) 23, 2018
262018
Ranking for Relevance and Display Preferences in Complex Presentation Layouts
H Oosterhuis, M de Rijke
SIGIR 2018: 41st international ACM SIGIR conference on Research and …, 2018
232018
Unifying online and counterfactual learning to rank: A novel counterfactual estimator that effectively utilizes online interventions
H Oosterhuis, M de Rijke
Proceedings of the 14th ACM International Conference on Web Search and Data …, 2021
212021
Balancing Speed and Quality in Online Learning to Rank for Information Retrieval
H Oosterhuis, M de Rijke
CIKM '17 ACM Conference on Information and Knowledge Management, 277-286, 2017
202017
When inverse propensity scoring does not work: Affine corrections for unbiased learning to rank
A Vardasbi, H Oosterhuis, M de Rijke
Proceedings of the 29th ACM International Conference on Information …, 2020
192020
Sensitive and Scalable Online Evaluation with Theoretical Guarantees
H Oosterhuis, M de Rijke
CIKM '17 ACM Conference on Information and Knowledge Management, 77-86, 2017
192017
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness
H Oosterhuis
Proceedings of the 44th International ACM SIGIR Conference on Research and …, 2021
172021
Optimizing ranking models in an online setting
H Oosterhuis, M Rijke
European Conference on Information Retrieval, 382-396, 2019
132019
Semantic video trailers
H Oosterhuis, S Ravi, M Bendersky
ICML 2016 Workshop on Multi-View Representation Learning, 2016
122016
Taking the counterfactual online: Efficient and unbiased online evaluation for ranking
H Oosterhuis, M de Rijke
Proceedings of the 2020 ACM SIGIR on International Conference on Theory of …, 2020
92020
Unbiased learning to rank: counterfactual and online approaches
H Oosterhuis, R Jagerman, M de Rijke
Companion Proceedings of the Web Conference 2020, 299-300, 2020
92020
Learning to Rank in Theory and Practice: From Gradient Boosting to Neural Networks and Unbiased Learning
C Lucchese, FM Nardini, RK Pasumarthi, S Bruch, M Bendersky, X Wang, ...
Proceedings of the 42nd International ACM SIGIR Conference on Research and …, 2019
82019
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